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Whole brain emulation (WBE) is a hypothetical theory that scans the mental state (long-term memory and “self”) of a particular brain and then transfer/copy to a computational substrate. Neuromorphic algorithms and neuromorphic engineering are attracting greater interest than before, since they showed powerful capability and inexorable progress in emulating brain. The former is also called brain-inspired algorithm, for example, artificial neural network, hierarchical perception, reinforcement learning, etc. The latter employed very-large-scale integration (VLSI) to emulate neurobiological architectures in brain.
Circuit integrations developed from the earliest semiconductor chip, then to the first integrated circuit, and to small-scale integration, further to medium-scale integration, and finally to large-scale and very-large-scale integrations. Nevertheless, even the VLSI cannot reach the computational complexity of emulating brains. Scholars have pointed out three-dimensional (3D) VLSI can be a possible means for continuing growth in VLSI, since conventional VLSI is confronted with tough challenges in economic and fundamental limits.
In contrast, spatial resolution in 3D printing is straightforward, although the current spatial resolution is far remove from nanoscale. Nevertheless, the industrial and academic experts in additive manufacturing take a positive altitude in the convergence of 3D printing and nanotechnology, which can easily print the neuristor-based 3D VLSI within reasonable overhead cost. Even the most difficult heat extraction problem is tractable by microfluidics approach in 3D printing.
The results of this simple thought experiment are promising, but still we will face many unpredictable technological problems. We have cogent reasons to aspire that the 3D printing in nanoscale can open the door for emulate brain in neuromorphic engineering.